With today's technology, computer users have easy access to thousands of digital images. As technology continues to advance, more and more computer users will have access to more and more images. However, as the number of images to which computer users have access increases, so does the difficulty in locating a particular image.
Attempts have been made to solve this problem. One solution is the use of keywords. There are several difficulties with the use of keywords in trying to locate an image. First, all images must be manually tagged. Second, some visual aspects of an image are hard to describe. Third, users must guess which keyword(s) were used to tag images of potential interest.
Based on the problems with keyword searching, other approaches have been used to describe images so that a database of images can be searched. One approach is to obtain a wavelet signature based on a wavelet decomposition to query by example. A wavelet signature describes the shape of objects located in an image First, the wavelet signature of a selected image that contains one or more features of interest to the user (such as an image of an object) is determined. Then, wavelet signatures are determined for all potentially similar images that may be of interest to the user contained in an image catalog (i.e., an image database) to be searched. Next, the wavelet signatures of the potentially similar images are compared with the wavelet signature of the selected image. The closer the wavelet signature of a potentially similar image is to the wavelet signature of the selected image, the greater the likelihood that the images are similar. Potentially similar images are ranked using this criteria and the result displayed, in order, to the user. There are some problems with this approach to image searching, for example, if an image is rotated, the original image and the rotated image will produce significantly different wavelet signatures.
Another approach to image searching employs color histograms. A color histogram provides general information about the color characteristics of an image. In this approach, the color histogram of a selected image that contains one or more features of interest to the user is determined. Then color histograms of all potentially similar images contained in an image catalog to be searched that may be of interest to the user are determined. The color histograms are compared, ranked and displayed, in order, to the user. There are also problems with this approach, for example, a color, a black and white, and a gray scale version of the same image will produce significantly different color histograms. Accordingly, there exists a need for an easy method to more accurately locate images that are similar to a selected image.